ERA: Meta Representation Alignment for Data Bias Mitigation in Recommendations

Recommender systems are widely used to help users discover content of interest. However, due to their reliance on observational user-item interaction data, they often suffer from data bias. Such biases primarily stem from non-random exposure and users’ self-selection behavior, which distort the data distribution and lead to suboptimal performance of recommendation models. Existing debiasing methods, especially those based on loss reweighting strategies, have shown promising empirical results but still lack solid theoretical guarantees. In particular, they struggle to handle the complex, diverse, and often unidentifiable types of bias encountered in real-world scenarios. In this paper, we revisit the problem of unbiased recommendation from the perspective of data bias and propose a unified debiasing framework that mitigates the effect of bias by aligning the distribution of training data with that of unbiased data collected under randomized exposure. We provide a thorough analysis of the theoretical limitations of existing reweighting methods, and we further propose a principled method, m E ta R epresentation A lignment (ERA), aiming to alleviate the inconsistency between user and item features under different distributions. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of ERA .